Local Binary Pattern and Ant Colony Optimization Based Feature Dimensionality Reduction Technique for Face Recognition Systems

Feature dimensionality reduction is the process of minimizing the number of features in high dimensional feature space. It encompasses two vital approaches: feature extraction and feature selection. In face recognition domain, widely adopted face dimensionality reduction techniques include Principal component analysis, Discrete wavelet transform, Linear discriminant analysis and Gabor filters. However, the performances of these techniques are limited by strict requirement of frontal face view, sensitivity to signal shift and sample size, computational intensiveness amongst others. In this paper, a feature dimensionality reduction technique that employed Local binary pattern for feature extraction and Ant colony optimization algorithms for the selection of optimal feature subsets was developed. The developed technique identified and selected the salient feature subsets capable of generating accurate recognition. The average training time, recognition time and recognition rate obtained from the experiment on locally acquired face data using cross-validation evaluation approach indicate an efficient performance of the potential combination of both methods in a two-level technique for dimensionality reduction.